SS‐Norm: Spectral‐spatial normalization for single‐domain generalization with application to retinal vessel segmentation

Abstract

AbstractRetinal vessel segmentation is an important computer vision task for eye retinopathy diagnosis. In the real scenarios, most datasets of source domain and target domain have distribution deviation, and the model often fails to generate accurate segmentation results due to the lack of data variation in single‐source domain, which damages the generalization ability to unseen target domains and may mislead doctors or artificial intelligence model in the following diseases diagnosis. Feature normalization is one feasible solution which can standardize data into uniform and stable distribution without additional data. However, the existing methods like batch normalization, uniform the data by global parameters. This leads to insufficient representation of important semantic information in the local region. To address this problem, the authors propose the spectral‐spatial normalization (SS‐Norm) module to enhance the generalization ability of the model. More specifically, the authors perform a discrete cosine transform (DCT) to decompose the feature into multiple frequency components and to analyze the semantic contribution degree of each component. By learning a spectral vector, the authors reweight the frequency components of features and therefore normalize the distribution in the spectral domain. Extensive experiments on six datasets prove the effectiveness of the authors’ methods.